Insights on enterprise AI, automation, and Odoo from the Trobz AI team.
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Your best Odoo developer has a prompt they retype every morning, and it lives nowhere but their head. A skill is how you take that knowledge out of the chat box and make it reusable.
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Security needs to be part of RAG chatbot design from day one. Here's how guardrails work, where to place them, and how to handle them without leaking information to attackers.
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Basic RAG breaks in predictable ways. Here's how we replaced a pile of conditional patches with an agentic graph pipeline, and what it actually cost in latency, complexity, and accuracy.
A distribution company hired us to automate purchase order approvals with AI. Two weeks later, we told them not to. Here's why that was the right outcome — and what we built instead.
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Pure vector search misses exact keyword matches; pure BM25 misses semantic intent. Hybrid search combines both — here's how to implement it on Odoo data with pgvector and PostgreSQL full-text search.
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A PoC that works on your laptop is not a production system. The gap between the two is wider than most stakeholders expect, and it shows up in six predictable places.
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After processing 3,000+ Vietnamese B2B contracts, the clean PDFs were never the problem. Here's what actually broke our OCR pipeline — and what we did about it.
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A step-by-step guide to building an Odoo support agent that retrieves product data, pricing rules, and quote history to answer sales rep questions accurately — and knows when to say it doesn't know.
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Most AI PoCs fail not because the model was bad, but because nobody agreed upfront what 'success' meant. One page fixes that.
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Our demand forecasting model correctly flagged a stockout risk three weeks out. It also generated 40 false positives — 12 of them caused by a data entry error the model had silently learned. Here's how we ran the retrospective.
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Odoo's built-in full-text search misses conceptually related content. Here's how to build semantic search over knowledge.article using pgvector and sentence-transformers — end to end, with benchmarks.
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Most teams evaluate RAG by asking: does it answer correctly? That question catches less than half the failures. Here's the full evaluation stack.
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Agents don't read PDFs. They read text. Here's what happens when you feed them one, and how to convert PDFs to Markdown without silently losing structure.
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A procurement automation project initially scoped at 6 months was reframed in 2 weeks. Real Odoo data, real accuracy numbers, and a conversation that changed the moment there was something to measure.
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In a long Claude Code session, most tokens go to re-reading conversation history, not generating output. Here is how to measure and fix that.
Most AI consultancies deliver a slide deck and call it discovery. We deliver a running system. Here's what happens in the 14 days between kickoff and go/no-go.
Collaborative filtering on your existing sale.order.line data is enough to build a working product recommendation engine — no external ML platform, no separate vector database. Here's how to implement it end-to-end inside Odoo eCommerce.
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Autonomous agents that can't be overridden won't be trusted -- and agents that aren't trusted won't be used. Human-in-the-loop isn't a design compromise; it's the feature that makes deployment possible.
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When three warehouses feed one Slack channel, every exception looks equally urgent. Here's how we separated the noise from the fires — and cut critical response time from 4 hours to 18 minutes.
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Your Odoo database already has everything needed for a 6-week cash flow forecast. Here's how to build the ML pipeline from raw data to a live dashboard widget.
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Gartner's composable ERP model points in the right direction. But after implementing it on top of Odoo, the gaps in the theory become obvious fast.
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A finance team deployed anomaly detection on their Odoo Accounting data. In the first week, it flagged a duplicate vendor bill for $40,000 that had already passed manual review twice.
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On April 23, the Trobz team attended the Alibaba Cloud AI Growth Workshop in Ho Chi Minh City: a morning of sharp insights on cloud infrastructure, Qwen AI models, and what Generative AI looks like at global scale.
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Em dashes have become the most-cited tell of AI writing. Search-and-replace is the wrong fix — the problem is the sentence structure underneath them.
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Bills of lading arrive in dozens of formats, but your Odoo Inventory needs clean, structured data. Here's how to build a pipeline that goes from PDF scan to stock.picking automatically.
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Trobz has accumulated over 8,000 photos across 15 years of events, trips, and photoshoots. We built an AI-powered search tool to find and rank the best ones — so we can stop relying on stock photos for our own blog.
Most AI roadmaps start with the technology. The ones that actually ship start with a specific process that's costing someone real hours every week.
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Most overdue invoice processes fail not because the team is lazy but because the workflow is inconsistent. Here's how an AI agent changes that — and what the first month looks like.
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RAG bridges the gap between LLMs and private enterprise knowledge. This article breaks down the architecture, design factors, trade-offs, and ROI decision matrix for building RAG into your business AI stack.
Keyword search breaks down when your sales reps don't know exact product names. Here's how to add semantic natural-language search to your Odoo product catalogue using the field_vector OCA module — no extra database required.
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Every AI vendor promises control and auditability. We chose open-source tools that actually deliver them — n8n, pgvector, and Transformers.js — and here's the reasoning behind each decision.
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An ML model trained on Odoo payment history predicted late payers with 79% precision — and cut Days Sales Outstanding by 11 days in four months.
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Poll Odoo's stock.quant model via JSON-RPC, apply per-product reorder rules, group alerts by warehouse, and deliver one consolidated Slack or email notification — no Odoo customization required.
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Foreign key constraints, sequence locking, fiscal year gates, and multi-company isolation are not just implementation details. They represent 20 years of encoded business rules. An AI-generated schema misses exactly the constraints that matter most.
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Claude Code goes sideways once its context window fills past 40%. Research, Plan, Implement: three sessions, three files, keeps it on track.
A 200-person services firm was spending 3,000+ staff-hours a year manually keying expense receipts into Odoo. Here's the Document AI pipeline that cut that to near zero.
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Bank reconciliation is one of the highest-ROI targets for an Odoo AI agent — repetitive, high-volume, but nuanced enough that the matching logic matters. Here's exactly how we built it.
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Most enterprise AI PoCs are built to impress a room, not to run in production. The gap between a working demo and a deployed system involves monitoring, retraining pipelines, error handling, and ownership — none of which appear in a two-week sprint.
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A SaaS sales team was spending 60% of its time on leads that never converted. AI lead scoring built directly into Odoo CRM changed that — without a data warehouse or a dedicated data team.
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Your Odoo database already contains the signals that predict customer churn. This tutorial shows how to extract them, train a classifier, and surface risk scores directly in the CRM kanban view — no data warehouse required.
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Most AI projects don't fail because of bad models or wrong tools. They fail because they should never have started. Here are five patterns that tell us when to walk away.
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A procurement team was drowning in email-based PO approvals — 47 a day, scattered across inboxes, with no audit trail. Here's how n8n and Odoo fixed it.
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I tested two popular MCP web search servers, Tavily and Brave Search, while building AI agents with Claude. Here's what I found, with real benchmark numbers and a clear recommendation.
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Several MCP implementations exist for connecting AI assistants to Odoo. The tooling is improving fast. Authentication is the hardest part to get right.
Vendors claim their ERP is "AI-native." The phrase means nothing precise and obscures the question that actually matters: does the integration layer let you connect AI capabilities as they evolve?
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A Hanoi-based distributor was spending 4 staff-hours a day keying in vendor invoices. A Document AI pipeline brought that to 12 minutes. Here is what it took.
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Odoo SA and the OCA take fundamentally different approaches to integrating AI. One bundles AI into the platform. The other keeps it deliberately outside. Here is what that looks like in practice.
Five production-ready n8n workflows for Odoo Finance: invoice reminders, reconciliation alerts, budget notifications, expense routing, and month-end triggers — all built on the same JSON-RPC pattern.
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Most AI projects in Odoo fail before a single model runs. The problems are always upstream: dirty data, undefined processes, nobody who owns the outcome. Here's how to find them first.
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AI agents can now replace the manual post-deploy verification step. But which agent, and at what level of access? We ran three against the same Odoo 18 ticket to find out.
Vibe coding feels powerful, until you inherit someone else's 800-line VBA macro. The AI moment rhymes with the spreadsheet moment in ways that should make every engineering team pay attention.
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n8n + Odoo 18 automation that flags confirmed but uninvoiced sales orders: one consolidated email every 15 minutes, not one per order.
Traditional monolithic ERP systems were not designed to absorb AI capabilities incrementally. Composable ERP, built from interchangeable API-connected modules, is the architecture that makes it possible.
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Someone always builds the thing you said couldn't be built. But there's a precise difference between an impressive invoicing app and an ERP, and it matters more than most people realise.
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How we built a fully client-side semantic search engine for 3,000+ Odoo modules with no backend, no API key, no internet required after first load.
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Discover how the field_vector OCA module brings native vector field support to Odoo, enabling semantic search and RAG without a separate vector database.
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